Satellite Monitoring of the State of Serbian Spruce (Picea omorika (Panč.) Purk.) Stands in the Mount Veliki Stolac Area (Republic of Srpska)

Cover Page

Cite item

Full Text

Abstract

Multispectral satellite images of medium spatial resolution are the main source of data for remote sensing of stands, including the assessment of forest inventory and biological productivity parameters of stands, as well as changes in the vital status of species. The aim of this work has been to determine the effects of pyrogenic impact on one of the largest populations of Serbian spruce (Picea omorika (Panč.) Purk.) in the Mount Veliki Stolac area (the Republic of Srpska, 1,675 mabove sea level) using multi-temporal multispectral Sentinel-2 imagery. Serbian spruce (Picea omorika (Panč.) Purk.) is a relict, endangered tree species whose total population has been declining significantly over the past 100 years. Currently, the natural habitat of this species is limited to a fairly small area on the border of Serbia and Bosnia and Herzegovina. To analyze satellite data, a multi-stage method has been proposed that has allowed identifying the Serbian spruce population in the surveyed areas, determining the dynamics of changes in vital status over the past 10 years, and assessing the effects of the forest fire that occurred in this area in 2021. It has been revealed that approximately 50 % of Serbian spruce stands in the study area have been damaged, with the species predicted to die for 1/2 of these areas. The greatest damage has been caused to the stands in the central part of the northern slope of Mount Veliki Stolac. Average estimates of the areas of vital status for the recovery period are: healthy – 17.6 ha, weakened – 8.4 ha, damaged – 8.0 ha, drying out – 1.2 ha. The analysis of vegetation indices has shown the absence of significant trends towards natural regeneration of Serbian spruce. The analysis of images for 2024 allowsus to reasonably assume that the process of replacing Serbian spruce with deciduous species has begun, while no improvement in the vital status of the Serbian spruce population is expected. Thus, in order to preserve this population, it is necessary to carry out reforestation of this valuable relict species.

About the authors

E. V Dmitriev

Moscow Institute of Physics and Technology (National Research University); Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences

Email: yegor@mail.ru
ORCID iD: 0000-0001-5363-3934
ResearcherId: E-4794-2014
Candidate of Physics and Mathematics, Assoc. Prof., Senior Research Scientist per. Institutskiy, 9, Dolgoprudny, Moscow Region, 141701, Russian Federation; ul. Gubkina, 8, Moscow, 119333, Russian Federation

Z. V Govedar

University of Banja Luka; Academy of Sciences and Arts of the Republic of Srpska

Email: zoran.govedar@sf.unibl.org
ORCID iD: 0000-0001-9791-4113
ResearcherId: AAH-6314-2019
Corresponding Member of the Academy of Sciences and Arts of the Republic of Srpska, Doctor of Agriculture, Prof. blv. Petar Bojovic, 1a, Banja Luka, Republic of Srpska, 78000, Bosnia and Herzegovina; blv. Ban Lazarevic, 1, Banja Luka, Republic of Srpska, 78000, Bosnia and Herzegovina

P. G Melnik

Bauman Moscow State Technical University (Mytishchi Branch)

Email: melnik_petr@bk.ru
ORCID iD: 0000-0002-2802-7614
ResearcherId: E-7644-2014
Candidate of Agriculture, Assoc. Prof. ul. 1-ya Institutskaya, 1, Mytishchi, Moscow Region, 141005, Russian Federation

T. V Kondranin

Moscow Institute of Physics and Technology (National Research University)

Email: kondr@kondr.rector.mipt.ru
ORCID iD: 0000-0003-3565-3194
ResearcherId: K-9407-2013
Doctor of Physics and Mathematics, Prof. per. Institutskiy, 9, Dolgoprudny, Moscow Region, 141701, Russian Federation

References

  1. Дмитриев Е.В., Козуб В.А., Мельник П.Г., Соколов А.А., Сафонова А.Н. Классификация и оценка состояния смешанных древостоев по аэроизображениям сверхвысокого пространственного разрешения // Лесн. журн. 2019. № 5. С. 9–24. Dmitriev E.V., Kozub V.A., Melnik P.G., Sokolov A.A., Safonova A.N. Classification and Assessment of the State of Mixed Forests from Very High Spatial Resolution Airborne Images. Lesnoy Zhurnal = Russian Forestry Journal, 2019, no. 5, pp. 9–24. (In Russ.). https://doi.org/10.17238/issn0536-1036.2019.5.9
  2. Дмитриев Е.В., Мельник П.Г., Донской С.А., Кондранин Т.В. Повышение эффективности текстурной сегментации лесного полога по изображениям сверхвысокого пространственного разрешения // Лесн. вестн. / Forestry Bulletin. 2023. Т. 27, № 5. С. 25–36. Dmitriev Y.V., Melnik P.G., Donskoy S.A., Kondranin T.V. Improving Efficiency of Tree Canopy Texture Segmentation by Using Very High Spatial Resolution Satellite Images. Lesnoy vestnik = Forestry Bulletin, 2023, vol. 27, no. 5, pp. 25–36. (In Russ.). https://doi.org/10.18698/2542-1468-2023-5-25-36
  3. Кобельков М.Е., Чуканов М.А., Хотин Д.В. Категории состояния основных лесообразующих пород Московской области. М., 2000. 40 с. Kobel’kov M.E., Chukanov M.A., Khotin D.V. Condition Categories of the Main Forest-Forming Species in the Moscow Region. Moscow, 2000. 40 p. (In Russ.).
  4. Коломыц Э.Г., Севостьянов С.М. Ландшафтно-экологическая стратегия геосистемного мониторинга лесов в условиях современного потепления // Проблемы региональной экологии. 2022. № 4. С. 39–47. Kolomyts E.G., Sevost’yanov S.M. Landscape-Ecological Strategy for Forest Geosystem Monitoring under the Conditions of Modern Warming. Problemy regionalnoj ekologii = Regional Environmental Issues, 2022, no. 4, pp. 39–47. (In Russ.). https://doi.org/10.24412/1728-323X-2022-4-39-47
  5. Кривец С.А., Керчев И.А., Бисирова Э.М., Пашенова Н.В., Демидко Д.А., Петько В.М., Баранчиков Ю.Н. Уссурийский полиграф в лесах Сибири (распространение, биология, экология, выявление и обследование поврежденных насаждений): метод. пособие. Томск; Красноярск, 2015. 48 c. Krivets S.A., Kerchev I.A., Bisirova E.M., Pashenova N.V., Demidko D.A., Pet’ko V.M., Baranchikov Yu.N. Four-Eyed Fir Bark Beetle in Siberian Forests (Distribution, Biology, Ecology, Detection and Survey of Damaged Stands): Study Guide. Tomsk, Krasnoyarsk, 2015. 48 p. (In Russ.).
  6. Лазарева А.А., Слаута А.А., Афонина Т.Е. Мониторинг геосистем особо охраняемых природных территорий в условиях антропогенной нагрузки // Наука. Инновации. Технологии. 2019. № 1. С. 35–46. Lazareva A.A., Slauta A.A., Afonina T.E. Monitoring Geosystems of Specially Protected Natural Territories in the Conditions of Anthropogenous Load. Nauka. Innovatsii. Tekhnologii = Science. Innovations. Technologies, 2019, no. 1, pp. 35–46. (In Russ.). https://doi.org/10.37495/2308-4758-2019-1-35-46
  7. Осовский С. Нейронные сети для обработки информации / пер. с пол. И.Д. Рудинского. М.: Финансы и статистика, 2004. 343 с. Osovskij S. Neural Networks for Information Processing. Trans. from Pol. by I.D. Rudinskij. Moscow, Finansy i statistika Publ., 2004. 343 p. (In Russ.).
  8. Adamović L. Die Vegetationsverhältnisse der Balkanländer (Mösische Länder): umfassend Serbien, Altserbien, Bulgarien, Ostrumelien, Nordthrakien und Nordmazedonien. Leipzig, Engelmann Verlag, 1909. 567 p. (In Germ.).
  9. Adjognon G.S., Rivera-Ballesteros A., van Soest D. Satellite-Based Tree Cover Mapping for Forest Сonservation in the Drylands of Sub Saharan Africa (SSA): Application to Burkina Faso Gazetted Forests. Development Engineering, 2019, vol. 4, art. no. 100039. https://doi.org/10.1016/j.deveng.2018.100039
  10. Aleksić J.M., Ballian D., Isajev D., Mataruga M., Christian T., Gardner M. Picea omorika. The IUCN Red List of Threatened Species, 2017. https://doi.org/10.2305/IUCN.UK.2017-2.RLTS.T30313A84039544.en
  11. Ballian D. Kontrola podrijetla Pančićeve omorike (Picea omorika/ Panč./ Purk.) iz plantaže kod Kaknja pomoću izoenzimskih biljega. Šumarski list, 2006, vol. 130, no. 7–8, pp. 295–304. (In Serb.).
  12. Barnes E.M., Clarke T.R., Richards S.E., Colaizzi P.D., Haberland J., Kostrzewski M., Waller P., Choi C., Riley E., Thompson T., Lascano R.J., Li H., Moran M.S. Coincident Detection of Crop Water Stress, Nitrogen Status and Canopy Density Using Ground-Based Multispectral Data. Proceedings of the Fifth International Conference on Precision Agriculture. USA, Minnesota, Bloomington, 2000. 15 p.
  13. Boiarskii B., Hasegawa H. Comparison of NDVI and NDRE Indices to Detect Differences in Vegetation and Chlorophyll Content. Journal of Mechanics of Continua and Mathematical Sciences, 2019, spec. iss. no. 4, pp. 20–29. https://doi.org/10.26782/jmcms.spl.4/2019.11.00003
  14. Burschel P. Die Omorikafichte. Forstarchiv, 1965, vol. 36. pp. 113–131. (In Germ.).
  15. Čolić D. Porijeklo i sukcesija šumskih zajednica sa Pančićevom omorikom (Picea omorika Panč.) na planini Tari. Zaštita prirode, 1965, vol. 29–30, pp. 65–90. (In Serb.).
  16. Čolić D. Požar kao ekološki faktor u sukcesiji zajednica Pančićeve omorike i redukovanju njenog areala. Zaštita prirode, 1966, vol. 33, pp. 1–167. (In Serb.).
  17. Čolić D. Spontana obnova Pančićeve omorike (Picea omorika Panč.) posle požara. Zaštita prirode, 1987, vol. 40, pp. 37–56. (In Serb.).
  18. Dietterich T.G., Bakiri G. Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research, 1995, vol. 2, pp. 263–286. https://doi.org/10.1613/jair.105
  19. Dizdarević M., Lakušić R., Grgić P., Kutleša L., Pavlović B., Jonlija R. Ekološke osnove poimanja reliktnosti vrste Picea omorica Pančić. Bilten Društva ekologa BiH, Serija A, 1984, vol. 2, pp. 7–56. (In Serb.).
  20. Dmitriev E.V. Classification of the Forest Cover of Tver Oblast Using Hyperspectral Airborne Imagery. Izvestiya, Atmospheric and Oceanic Physics, 2014, vol. 50, pp. 929–942. https://doi.org/10.1134/s0001433814090072
  21. Dmitriev E.V., Kondranin T.V., Zotov S.A. Segmentation of Natural and Anthropogenic Objects by Panchromatic Satellite Images Using Statistical Textural Features. Optoelectronics, Instrumentation and Data Processing, 2022, vol. 58, pp. 167–179. https://doi.org/10.3103/s8756699022020029
  22. Fukarek P. Staništa Pančićeve omorike nakon šumskih požara u 1946/47 godini. Šumarski list, 1951, vol. 75, pp. 61–75. (In Serb.).
  23. Fukarek P., Fukarek Đ. Sukcesivni niz subasocijacija unutar zajednica omorike (Piceetum omorikae Tregubov, emend. P. Fukarek) u njenim sastojinama u istočnoj Bosni. Šumarski list, 1989, vol. 113, no. 11–12, pp. 567–580. (In Serb.).
  24. Gao B.-с. NDWI – A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing of Environment, 1996, vol. 58, iss. 3, pp. 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3,1996.A
  25. Gitelson A.A., Viña A., Ciganda V., Rundquist D.C., Arkebauer T.J. Remote Estimation of Canopy Chlorophyll Content in Crops. Geophysical Research Letters, 2005, vol. 32. iss. 8. art. no. L08403. https://doi.org/10.1029/2005GL022688
  26. Govedar Z., Krstić M., Stanivuković Z. Categorisation of Trees in Serbian Spruce Natural Stands in the Region of Govza in the Republic of Srpska. 50 Godini Lesotehničeski Universitet. Bulgaria, Sofia, 2003, pp. 96–101.
  27. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction: 2nd ed. New York, Springer, 2009. 745 p. https://doi.org/10.1007/978-0-387-84858-7
  28. Hou W., Li D., Xu C., Zhang H., Li T. An Advanced k Nearest Neighbor Classification Algorithm Based on KD-tree. 2018 IEEE International Conference of Safety Produce Informatization (IICSPI). China, Chongqing, 2018, pp. 902–905. https://doi.org/10.1109/IICSPI.2018.8690508
  29. IUCN, 2021. The IUCN Red List of Threatened Species. Version 2020-3. Available at: https://www.iucnredlist.org/ (accessed 15.08.24).
  30. Ivetić V., Aleksić J.M. Response of Rare and Endangered Species Picea omorika to Climate Change – The Need for Speed. Reforesta, 2016, vol. 2, pp. 81–99. https://doi.org/10.21750/REFOR.2.09.24
  31. Keeley J.E. Fire Intensity, Fire Severity and Burn Severity: a Brief Review and Suggested Usage. International Journal of Wildland Fire, 2009, vol. 18, iss. 1, pp. 116–126. https://doi.org/10.1071/WF07049
  32. Knipling E.B. Physical and Physiological Basis for the Reflectance of Visible and Near-Infrared Radiation from Vegetation. Remote Sensing of Environment, 1970, vol. 1, iss. 3, pp. 155–159. https://doi.org/10.1016/S0034-4257(70)80021-9
  33. Mataruga M., Milanović Đ., Ćurić A. Smjernice za očuvanje Pančićeve omorike – in situ. Republic Institute for the Protection of Cultural, Historical and Natural Heritage, 2024. 83 p. (In Serb.).
  34. Mataruga M., Piotti A., Daničić V., Cvjetković B., Fussi B., Konnert M., Vendramin G.G., Aleksić J.M. Towards the Dynamic Conservation of Serbian Spruce (Picea omorika) Western Populations. Annals of Forest Science, 2020, vol. 77, art. no. 1. https://doi.org/10.1007/s13595-019-0892-1
  35. Maly M. Beiträge zur Kenntnis der Picea omorika. Glasnik Zemallj, Muzeja BiH, 1934, vol. 46, pp. 37–64. (In Germ.).
  36. Pančić J. Omorika nova fela četinara u Srbiji. Beograd, Težak, 1887, vol. XVIII, no. 1, pp. 1–8. (In Serb.).
  37. Rasmussen C.E., Williams C.K.I. Gaussian Processes for Machine Learning. Massachusetts, Cambridge, MIT Press, 2006. 247 p. https://doi.org/10.7551/mitpress/3206.001.0001
  38. Rouse J.W. Jr., Haas R.H., Scheel J.A., Deering D.W. Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of the 3rd Earth Resource Technology Satellite-1 (ERTS-1) Symposium, 1974, vol. 1, pp. 48–62.
  39. Shaw R.G., Etterson J.R. Rapid Climate Change and the Rate of Adaptation: Insight from Experimental Quantitative Genetics. New Phytologist, 2012, vol. 195, iss. 4, pp. 752–765. https://doi.org/10.1111/j.1469-8137.2012.04230.x
  40. Scogings P.F. Perspective: Monitoring Global Forests Using Only Structural Metrics – Problems and Solutions from a Savanna Viewpoint. Forest Ecology and Management, 2023, vol. 546, art. no. 121381. https://doi.org/10.1016/j.foreco.2023.121381
  41. Sims D.A., Gamon J.A. Relationships between Leaf Pigment Content and Spectral Reflectance across a Wide Range of Species, Leaf Structures and Developmental Stages. Remote Sensing of Environment, 2002, vol. 81, iss. 2–3, pp. 337–354. https://doi.org/10.1016/S0034-4257(02)00010-X
  42. Stojanović O. Prirast i oblik stabla Pančićeve omorike na njenom prirodnom staništu. Radovi Šumarskog fakulteta i Instituta za šumarstvo i drvnu industriju u Sarajevu, 1959, vol. 7(4), pp. 163–188. (In Serb.). https://doi.org/10.54652/rsf.1959.v7.i4.403
  43. Taloor A.K., Manhas D.S., Kothyari G.C. Retrieval of Land Surface Temperature, Normalized Difference Moisture Index, Normalized Difference Water Index of the Ravi Basin using Landsat Data. Applied Computing and Geosciences, 2021, vol. 9, art. no. 100051. https://doi.org/10.1016/j.acags.2020.100051
  44. Talukdar N.R., Ahmad F., Goparaju L., Choudhury P., Arya R., Qayum A., Rizvi J. Forest Fire Estimation and Risk Prediction Using Multispectral Satellite Images: Case Study. Natural Hazards Research, 2024, vol. 4, iss. 2, pp. 304–319. https://doi.org/10.1016/j.nhres.2024.01.007
  45. Tomppo E., Olsson H., Ståhl G., Nilsson M., Hagner O., Katila M. Combining National Forest Inventory Field Plots and Remote Sensing Data for Forest Databases. Remote Sensing of Environment, 2008, vol. 112, iss. 5, pp. 1982–1999. https://doi.org/10.1016/j.rse.2007.03.032
  46. Tošić M. O jednom novom nalazištu pančićeve omorike (Picea omorika Pančić) u Srbiji. Radovi ANUBiH. Odjeljenje prirodnih i tehničkih nauka, 1983, vol. 72(21), pp. 267–274. (In Serb.).
  47. Vapnik V.N. The Nature of Statistical Learning Theory: 2nd ed. New York, Springer, 1999. 314 p. https://doi.org/10.1007/978-1-4757-3264-1
  48. Wang S., Li H., Niu S. Empirical Research on Climate Warming Risks for Forest Fires: A Case Study of Grade I Forest Fire Danger Zone, Sichuan Province, China. Sustainability, 2021, vol. 13, no. 14, art. no. 7773. https://doi.org/10.3390/su13147773
  49. Wang X.-Q., Ran J.-H. Evolution and Biogeography of Gymnosperms. Molecular Phylogenetics and Evolution, 2014, vol. 75, pp. 24–40. https://doi.org/10.1016/j.ympev.2014.02.005
  50. Wang H., Shao X.-m., Jiang Y., Fang X.-q., Wu S.-h. The Impacts of Climate Change on the Radial Growth of Pinus koraiensis along Elevations of Changbai Mountain in Northeastern China. Forest Ecology and Management, 2013, vol. 289, pp. 333–340. https://doi.org/10.1016/j.foreco.2012.10.023
  51. Wettstein R. Das Vorkommen der Picea Omorica (Panč.) Willk. in Bosnien. Oesterreichische botanische Zeitschrift, 1890a, vol. 40, no. 10, pp. 357–361. (In Germ.).
  52. Wettstein R. Die Omorika-Fichte, Picea omorika (Panč.). Eine monographische Studie. Sitzungsberichte der Kaiserlichen Akademie der Wissenschaften, 1890b, vol. 99, no. 10, pp. 502–565. (In Germ.).
  53. Woinarski J.C.Z., Legge S., Lindenmayer D.B., Robinson N.M., Scheele B.C., Southwell D.M., Wintle B.A. A Framework for Evaluating the Adequacy of Monitoring Programs for Threatened Species. Monitoring Threatened Species and Ecological Communities. Clayton, CSIRO Publ., 2018, pp. 13–20.
  54. Woodmansee R.G., Wallach L.S. Effects of Fire Regimes on Biogeochemical Cycles. Fire Regimes and Ecosystem Properties: Proceedings of the Conference. Washington, D.C., USDA Forest Service, General Technical Report WO-GTR-26, 1981, pp. 379–400.
  55. Xie D., Du H., Xu W.-H., Ran J.-H., Wang X.-Q. Effects of Climate Change on Richness Distribution Patterns of Threatened Conifers Endemic to China. Ecological Indicators, 2022, vol. 136, art. no. 108594. https://doi.org/10.1016/j.ecolind.2022.108594
  56. Xu Y., Zang R. Conservation of Rare and Endangered Plant Species in China. iScience, 2023, vol. 26, iss. 2, art. no. 106008. https://doi.org/10.1016/j.isci.2023.106008

Supplementary files

Supplementary Files
Action
1. JATS XML
Download


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).